University Of Phoenix Material Week 4 Practice Problems

University Of Phoenix Materialweek 4 Practice Problems Worksheetplease

Answer the following questions in at least 150 words, referencing the provided information as needed. Log in to the specified website using the email [email protected] and password ion123 to access the textbook and related materials. Use the data provided for each problem to perform relevant t-tests or ANOVA tests, and interpret the results in layman's terms, explaining concepts like hypotheses, significance levels, p-values, and why specific tests are appropriate for each scenario.

Paper For Above instruction

The provided assignment involves analyzing multiple data sets using statistical tests such as t-tests and ANOVA, with an emphasis on interpreting results for individuals unfamiliar with complex statistical procedures. The first problem involves comparing two sailing teams’ times using an independent t-test with unequal variances. The data, consisting of sample times for Prada (Italy) and Oracle (USA), are analyzed to determine if a statistically significant difference exists in their mean racing times at the 0.05 significance level.

In layman's terms, a t-test compares the averages of two groups to see if they are different enough that the difference isn’t just due to random chance. Because the data may have different variances, an unequal variances t-test (also known as Welch’s t-test) is suitable. The results show a t-value of approximately -2.705 and a p-value near 0.0017, which is less than 0.05. This indicates a statistically significant difference, meaning we have strong evidence that Prada’s and Oracle’s mean times differ in the race.

Next, the problem involves the impulse spending of customers in two Haggar Outlet Stores. Using the same form of t-test, data on the amounts spent at Peach Street and Plum Street stores are compared at a 0.01 significance level. Here, the calculations include means, standard deviations, and sample sizes. The results show a p-value of approximately 0.0255, which is less than 0.01, meaning a significant difference exists in the average amount spent impulsively between the two store locations. This indicates that layout or store design might influence spontaneous purchases.

The third problem assesses whether Larry Clark and George Murnen, who repair furnaces and air conditioning units, make a different number of service calls per day. Known population standard deviations are provided, and a pooled variance t-test is conducted at the 0.05 significance level. The calculations yield a p-value near 0.3098, which is greater than 0.05. Thus, there is no statistically significant difference in the average number of calls made per day by the two technicians, suggesting their work volume is comparable over time.

Deciding between dependent and independent t-tests involves evaluating whether the data points are related. For example, measuring the heights of firstborn students from large and small families involves independent groups because the students are distinct. Testing pre- and post-seminar math scores involve dependent (paired) data because the same individuals are measured before and after. Comparing the resting heart rate of drug users vs. non-users involves independent groups, as these are separate populations.

Moving to ANOVA, the analysis of toy prices across three store types reveals whether a significant difference exists among their means. The one-way ANOVA tests the hypothesis that all group means are equal by examining variances within and between groups. The results, with a p-value below 0.05, suggest at least one store type’s average price differs significantly. This method is appropriate because the comparison involves more than two groups.

Finally, a weight loss study compares three different diets through ANOVA. The F-test determines whether observed differences in mean weight loss are statistically significant. A p-value smaller than 0.01 confirms significant differences among the diets. To identify specifically which diets differ, further analysis like post-hoc tests (e.g., Tukey’s HSD) is needed. This helps pinpoint exactly where the differences lie, providing more targeted insights into diet efficacy.

References

  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • McDonald, J. H. (2014). Handbook of Biological Statistics. Sparky House Publishing.
  • Stein, R. (2019). The Practice of Statistics. W. H. Freeman.
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics. W. H. Freeman.
  • Yadgon, H. (2018). Applied Statistics for Business and Economics. Cambridge University Press.
  • Laerd Statistics. (n.d.). T-tests in SPSS. Retrieved from https://statistics.laerd.com
  • Huber, P. J. (2009). Robust Statistics. Wiley.
  • Hays, W. L. (2016). Statistics. Harcourt Brace.
  • Grove, S. K., & Vandergust, R. (2012). Understanding and Applying Basic Statistical Methods. Routledge.
  • Warner, R. M. (2013). Applied Statistics: From Bivariate Through Multivariate Techniques. Sage Publications.

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